Commercial video summaries using crowd annotation

ABSTRACT

A system for executing a video summary is provided. One or more video segments for a video based on one or more digital media is generated. A video summary is generated based on a user request.

BACKGROUND OF THE INVENTION

The present invention relates generally to the field of video-sharing,and more particularly to video annotation.

In recent years, the growth of marketing for commercial products andservices has increased. Additionally, the presence of online marketinghas increased allowing consumers greater access to these marketingcampaigns, which has allowed consumers to further educate themselvesabout commercial products and services.

SUMMARY

Embodiments of the present invention provide a method, system, andprogram product for a system for executing a video summary is managed.

A first embodiment encompasses a method for a system for executing avideo summary is managed. One or more processors generate one or morevideo segments for a video based on digital media data. One or moreprocessors generate a video summary based on a user request.

A second embodiment encompasses a computer program product for a systemfor executing a video summary is managed. The computer program productincludes one or more computer-readable storage media and programinstructions stored on the one or more computer-readable storage media.The program instructions include program instructions to generate one ormore video segments for a video based on digital media data. The programinstruction includes program instructions to generate a video summarybased on a user request.

A third embodiment encompasses a computer system for a system forexecuting a video summary is managed. The computer system includes oneor more computer processors, one or more computer readable storagemedium, and program instructions stored on the computer readable storagemedium for execution by at least one of the one or more processors. Thecomputer program product includes one or more computer-readable storagemedia and program instructions stored on the one or morecomputer-readable storage media. The program instructions includeprogram instructions to generate one or more video segments for a videobased on digital media data. The program instructions include programinstructions to generate a video summary based on a user request.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

FIG. 1 is a functional block diagram illustrating a computingenvironment, in which a system for executing a video summary is managed,in accordance with an exemplary embodiment of the present invention.

FIG. 2 illustrates operational processes of a system for executing avideo summary, on a computing device within the environment of FIG. 1 ,in accordance with an exemplary embodiment of the present invention.

FIG. 3 illustrates operational processes of a system for executing avideo summary, on a computing device within the environment of FIG. 1 ,in accordance with an exemplary embodiment of the present invention.

FIG. 4 illustrates operational processes of a system for executing avideo summary, on a computing device within the environment of FIG. 1 ,in accordance with an exemplary embodiment of the present invention.

FIG. 5 depicts a cloud computing environment according to at least oneembodiment of the present invention.

FIG. 6 depicts abstraction model layers according to at least oneembodiment of the present invention.

FIG. 7 depicts a block diagram of components of one or more computingdevices within the computing environment depicted in FIG. 1 , inaccordance with an exemplary embodiment of the present invention.

DETAILED DESCRIPTION

Detailed embodiments of the present invention are disclosed herein withreference to the accompanying drawings. It is to be understood that thedisclosed embodiments are merely illustrative of potential embodimentsof the present invention and may take various forms. In addition, eachof the examples given in connection with the various embodiments isintended to be illustrative, and not restrictive. Further, the figuresare not necessarily to scale, some features may be exaggerated to showdetails of particular components. Therefore, specific structural andfunctional details disclosed herein are not to be interpreted aslimiting, but merely as a representative basis for teaching one skilledin the art to variously employ the present invention.

References in the specification to “one embodiment”, “an embodiment”,“an example embodiment”, etc., indicate that the embodiment describedmay include a particular feature, structure, or characteristic, butevery embodiment may not necessarily include the particular feature,structure, or characteristic. Moreover, such phrases are not necessarilyreferring to the same embodiment. Further, when a particular feature,structure, or characteristic is described in connection with anembodiment, it is submitted that it is within the knowledge of oneskilled in the art to affect such feature, structure, or characteristicin connection with other embodiments whether or not explicitlydescribed.

While possible solutions to video annotation summary are known, thesesolutions may be inadequate to generate a video annotation summarybased, at least, on crowd-based comments associated with video directaccesses. Embodiments of the present invention recognize that suchsolutions can be improved by analyzing crowd-based comments associatedwith video direct accesses that correlate with video segments andgenerate additional content that directly communicate the commercialproducts and/or services review that the consumer is searching for.

In general, commercial products and/or services that are marketedutilizing video-sharing webpages are often lengthy and contain numerousand vital pieces of information for a consumer. However, these videosare often filled with unnecessary and necessary dialogue that isinterjected between the numerous and vital information that the consumeris seeking. Generally, video annotation summaries using crowd-basedcomments are added in a comments section attached to the commercialproducts and/or services review.

Embodiments of the present invention recognize that in variousembodiments, natural language processing, word2vec, and cognitive AIprocessing represent models utilized to analyze crowd-based comments andvideo direct accesses to generate one or more video segments associatedwith a video. In various embodiments, word2vec is a group of relatedmodels that are used to produce word embeddings. These models areshallow, two-layer neural networks that are trained to reconstructlinguistic contexts of words. Word2vec takes as its input a large corpusof text and produces a vector space, typically of several hundreddimensions, with each unique word in the corpus being assigned acorresponding vector in the space. In various embodiments, cognitive AIprocessing is utilized to analyze and learn from the correspondingcrowd-based comments. Further, embodiments provide text processing toanalyze the crowd-based comments and video direct accesses to determinewhether one or more crowd-based comments and/or video direct accessesprovide substantive feedback associated, at least, with one segment ofthe video to generate a video summary. When text processing is utilizedto determine one or more video segments, it allows for a wide range offeedback to be captured by the present invention. Additionally, textprocessing allows the present invention to identify product reviews fromvarious consumers who purchased, will purchase, and/or have used acommercial product or service in the past.

Embodiments of the present invention recognize that video-sharingwebpages may not provide adequate detail to inform consumers aboutcommercial products and/or services. Embodiments provide videoannotation summaries based, at least, on crowd-based comments associatedwith video direct accesses. Further, embodiments provide for acompilation of video segments into a cohesive video annotation summarybased, at least, on crowd-based annotation comments and video directaccesses. When such as video annotation summary is generated it allowsfor consumers to readily access commercial product and/or servicereviews and marketing, which can then be leveraged by consumers toadequately make informed and responsible purchases. Such an approachoften increases the level of notoriety and sales of a commercial productand/or service, as well as adequately inform consumers about a productand/or service that they are searching for in particular.

In one embodiment of the present invention, video segmentation program122 generates one or more video segments for a video based on one ormore digital media data. Video segmentation program 122 generates avideo summary based on a user request.

In one embodiment of the present invention, video segmentation program122 receives a user request. Video segmentation program 122 executes aquery on a database based on the user request. Video segmentationprogram 122 identifies one or more digital media data associated withthe user request. Video segmentation program 122 receives the one ormore digital media.

In one embodiment of the present invention, video segmentation program122 analyzes the one or more digital media data. Video segmentationprogram 122 identifies data associated with the digital media data thatincludes (i) one or more videos, (ii) one or more crowd-based comments,and (iii) one or more video direct accesses. Video segmentation program122 generates an analysis result by analyzing the one or more digitalmedia data using one or a combination of (i) natural languageprocessing, (ii) word2vec, (iii) cognitive AI processing, (iv) videoprocessing, and (v) machine vision. Video segmentation program 122identifies one or more user feedback associated the one or more digitalmedia data based on the analysis result.

In one embodiment of the present invention, video segmentation program122 analyzes the one or more user feedback. Video segmentation program122 identifies one or more user feedback that reaches a threshold valueof similarity to the user request. Video segmentation program 122identifies that the one or more user feedback reaches a threshold valueof similarity based on a result of a semantic analysis of (i) the userrequest and (ii) the one or more user feedback.

In one embodiment of the present invention, video segmentation program122 generates one or more video segments based on the user feedbackassociated with the one or more digital media data. Video segmentationprogram 122 generates an aggregate of the one or more video segments,wherein the aggregate includes a video direct access that is associatedwith a specific timestamp at which a given video segment begins.

In one embodiment of the present invention, video segmentation program122 generates the video summary which includes (i) the aggregate of theone or more video segments, (ii) the one or more digital media data.Video segmentation program 122 generates one or more labels for thevideo summary based on (i) an analysis of a crowd-based comment thatpoints to a video segment and (ii) on one or a combination of words,hash representation, or n-gram structures. Video segmentation program122 generates a ranking for the one or more video segments based on acalculated score of the content of the one or more crowd-based comments,wherein the given calculated score is based on a feedforward neuralnetwork used to determine a weighting factor associated with featuresassociated with the one or more video segments.

In one embodiment of the present invention, video segmentation program122 plays the video summary for one or more users. Video segmentationprogram 122 receives user activity associated with the one or moreusers. Video segmentation program 122 analyzes a user activity. Videosegmentation program 122 updates the one or more video segmentsassociated with the video summary based on the view time of the one ormore video segments.

In one embodiment of the present invention, video segmentation program122 identifies one or more crowd-based interactions associated with theone or more videos. Video segmentation program 122 generates an analysisby analyzing the one or more crowd-based interactions that include oneor a combination of: (i) crowd-based interaction with the one or morevideos, (ii) one or more crowd-based comments associated with one ormore segments of the one or more videos, or (iii) one or morecrowd-based reactions associated with the one or more videos. Videosegmentation program 122 determines the one or more video segments basedon the analysis result.

The present invention will now be described in detail with reference tothe Figures.

FIG. 1 is a functional block diagram illustrating a computingenvironment, generally designated 100, in accordance with an embodimentof the present invention. Computing environment 100 includes computersystem 120, client device 130, and storage area network 140 connectedover network 110. Computer system 120 includes video segmentationprogram 122, video sharing application 124, and database 126. Clientdevice 130 includes client application 132 and computer interface 134.Storage area network (SAN) 140 includes server application 142 anddatabase 144.

In various embodiment of the present invention, computer system 120 is acomputing device that can be a standalone device, a server, a laptopcomputer, a tablet computer, a netbook computer, a personal computer(PC), a personal digital assistant (PDA), smartwatch, a desktopcomputer, servers, server-cluster, web servers or any programmableelectronic device capable of receiving, sending, and processing data. Ingeneral, computer system 120 represents any programmable electronicdevice or combination of programmable electronic devices capable ofexecuting machine readable program instructions and communicating withclient device 130 and SAN 140, and other computing devices (not shown)within computing environment 100 via a network, such as network 110. Inanother embodiment, computer system 120 represents a computing systemutilizing clustered computers, components to act as a single pool ofseamless resources. In general, computer system 120 can be any computingdevice or a combination of devices with access to client device 130 andSAN 140 and is capable of executing video segmentation program 122 andvideo sharing application 124. Computer system 120 may include internaland external hardware components, as depicted and described in furtherdetail with respect to FIG. 7 .

In this exemplary embodiment, video segmentation program 122 and videosharing application 124 are stored on computer system 120. However, inother embodiments, video segmentation program 122 and video sharingapplication 124 may be stored externally and accessed through acommunication network, such as network 110. Network 110 can be, forexample, a local area network (LAN), a wide area network (WAN) such asthe Internet, or a combination of the two, and may include wired,wireless, fiber optic, or any other connection known in the art. Ingeneral, network 110 can be any combination of connections and protocolsthat will support communications between computer system 120, clientdevice 130, and SAN 140, in accordance with a desired embodiment of thepresent invention.

Additionally, in some embodiments, computer system 120 represents acloud computing platform. Cloud computing is a model or service deliveryfor enabling convenient, on demand network access to a shared pool ofconfigurable computing resources (e.g., networks, network bandwidth,servers, processing, memory, storage, applications, virtual machines,and services) that can be rapidly provisioned and released with minimalmanagement effort or interaction with a provider of a service. A cloudmodel may include characteristics such as on-demand self-service, broadnetwork access, resource pooling, rapid elasticity, and measuredservice, can be represented by service models including a platform as aservice (PaaS) model, an infrastructure as a service (IaaS) model, and asoftware as a service (SaaS) model, and can be implemented as variousdeployment models including as a private cloud, a community cloud, apublic cloud, and a hybrid cloud.

In various embodiments of the present invention, client device 130 is acomputing device that can be a standalone device, a server, a laptopcomputer, a tablet computer, a netbook computer, a personal computer(PC), a personal digital assistant (PDA), a smartphone, a smartwatch,smart glasses, a desktop computer, or any programmable electronic deviceor combination of programmable electronic devices capable of executingmachine readable program instructions and communicating with computersystem 120, and SAN 140 and other computing devices (not shown) withincomputing environment 100 via a network, such as network 110. In anotherembodiment, client device 130 represents a computing system utilizingclustered computers and components to act as a single pool of seamlessresources. In general, client device 130 can be any programmable deviceor a combination of devices with access to computer system 120, SAN 140,and network 110 and is capable of executing client application 132 andcomputer interface 134. Client device 130 may include internal andexternal hardware components, as depicted and described in furtherdetail with respect to FIG. 7 .

Client device 130 includes computer interface 134. Computer interface134 provides an interface between client device 130, computer system120, and SAN 140. In some embodiments, computer interface 134 can be agraphical user interface (GUI) or web user interface (WUI) and candisplay text, documents, web browser, windows, user options, applicationinterfaces, and instructions for operations, and includes theinformation (such as graphic, text, and sound) that a program presentsto a user and the control sequences the user employs to control theprogram. In some embodiments, client device 130 accesses datacommunicated from computer system 120 and/or SAN 140 via client-basedapplication that runs on client device 130. For example, client device130 includes mobile application software that provides an interfacebetween client device 130, computer system 120, and SAN 140.

Storage area network (SAN) 140 is a storage system that includes serverapplication 142 and database 144. SAN 140 may include one or more, butis not limited to, computing devices, servers, server-clusters, webservers, databases and storage devices. SAN 140 operates to communicatewith computer system 120, client device 130, and various other computingdevices (not shown) over a network, such as network 110. For example,SAN 140 communicates with video segmentation program 122 to transferdata between, but is not limited to, computer system 120, client device130, and various other computing devices (not shown) that are connectedto network 110. SAN 140 can be any computing device or a combination ofdevices that are communicatively connected to a local IoT network, i.e.,a network comprised of various computing devices including, but are notlimited to computer system 120 and client device 130 to provide thefunctionality described herein. SAN 140 can include internal andexternal hardware components as described with respect to FIG. 7 . Thepresent invention recognizes that FIG. 1 may include any number ofcomputing devices, servers, databases, and/or storage devices, and thepresent invention is not limited to only what is depicted in FIG. 1 . Assuch, in some embodiments, some or all of the features and functions ofSAN 140 are included as apart of computer system 120, client device 130and/or another computing device. Similarly, in some embodiments, some ofthe features and functions of computer system 120 are included as partof SAN 140 and/or another computing device.

Additionally, in some embodiments, SAN 140 represents a cloud computingplatform Cloud computing is a model or service delivery for enablingconvenient, on demand network access to a shared pool of configurablecomputing resources (e.g., networks, network bandwidth, servers,processing, memory, storage, applications, virtual machines, andservices) that can be rapidly provisioned and released with minimalmanagement effort or interaction with a provider of a service. A cloudmodel may include characteristics such as on-demand self-service, broadnetwork access, resource pooling, rapid elasticity, and measuredservice, can be represented by service models including a platform as aservice (PaaS) model, an infrastructure as a service (IaaS) model, and asoftware as a service (SaaS) model; and can be implemented as variousdeployment models including as a private cloud, a community cloud, apublic cloud, and a hybrid cloud.

In various embodiments, SAN 140 is depicted in FIG. 1 for illustrativesimplicity. However, it is to be understood that, in variousembodiments, SAN 140 can include any number of databases that aremanaged in accordance with the functionality of server application 142.In general, database 144 represents data and server application 142represents code that provides an ability to take specific action withrespect to another physical or virtual resource and manages the abilityto use and modify the data. In an alternative embodiment, videosegmentation program 122 can also represent any combination of theaforementioned features, in which server application 142 has access todatabase 144. To illustrate various aspects of the present invention,examples of server application 142 are presented in which videosegmentation program 122 represents one or more of, but is not limitedto, a local IoT network and contract event monitoring system.

In this exemplary embodiment, server application 142 and database 144are stored on SAN 140. However, in other embodiments, server application142 and database 144 may be stored externally and accessed through acommunication network, such as network 110, as discussed above.

In the embodiment depicted in FIG. 1 , server application 142, at leastin part, has access to video segmentation program 122 and cancommunicate digital media data stored on SAN 140 to computer system 120and/or client device 130. Alternatively, video segmentation program 122has access to server application 142 and can communicate digital mediadata stored on computer system 120, SAN 140, and client device 130. Insome embodiments, computer system 120 and SAN 140 have access to variousother computing devices (not shown) and can communicate data stored,respectively on computer system 120, client device 130, and SAN 140 tothe various other computing devices. For example, video segmentationprogram 122 defines a video augmentation system for computer system 120that has access to digital media data on SAN 140 and has access todigital media data on other computer systems (e.g., various othercomputing devices).

In various embodiments, SAN 140 represents an internet-based service forvideo sharing. In various embodiments, SAN 140 encompasses software,servers, databases, web-servers, and web pages support by software tooperate and maintain an internet-based service for video sharing. Usersof the Internet have access to web pages maintained and supported by SAN140 via the Internet. One or more users of the Internet (i.e., crowd)have the availability to comment on various videos uploaded to SAN 140and accessible via the web pages, as well as upload videos to SAN 140,respectively.

In various embodiments depicted in FIG. 1 , digital media data is, atleast in part, obtained from client device 130 and/or SAN 140. Asdiscussed above, SAN 140 represents an internet-based service for videosharing. Digital media data is stored on database 144. However, invarious embodiments, digital media data can be stored on client device130 and/or stored on various other computing devices (not shown). Invarious embodiments, digital media data represents various videos storedon SAN 140, as well as the various crowd comments and various videodirect accesses stored on SAN 140. In some embodiments, digital mediadata represents various video summaries and video features associated,at least, with the various video summaries. In some embodiments, videosummaries represent video segments generated by video segmentationprogram 122 based, at least, on a user-generated request. Additionally,video features represent tags associated with the content of the variousvideo segments and video summaries.

In various embodiments, embodiments depicted in FIG. 1 , digital mediadata originates within SAN 140. The present invention recognizes thatdigital media data is associated, at least, with video-sharing uploadsand crowd-based comments that occur between the video-sharing system andthe crowd. In some embodiments, server application 142 communicates withcomputer system 120 and communicates digital media data to computersystem 120, wherein server application 142, at least, communicates withvideo segmentation program and/or video sharing application 124. In analternative embodiment, video segmentation program 122 communicates aset of program instructions to server application 142 instructing serverapplication 142 to transmit digital media data to computer system 120.In yet another embodiment, video segmentation program 122 communicateswith SAN 140 and retrieves digital media data from SAN 140.

In various embodiments of the present invention, a user of client device130 (hereinafter “requestor”) generates a user request and communicatesthe request to computer system 120. In various embodiments, the userrequest is associated with a specific content that the requestor isinterested in obtaining information with regards to the specificcontent. In some embodiments, the user request contains one or morekeyword searches associated with the specific content. Clientapplication 132 generates the user request based, at least in part, onthe one or more keyword searches and communicates the user request tocomputer system 120 to generate a video summary regarding the specificcontent.

In various embodiments, in response to computer system 120 receiving auser request from client device 130, video segmentation program 122analyzes the user request. Video segmentation program 122 identifies oneor more keyword searches associated with the user request. In variousembodiments, video segmentation program 122 communicates with SAN 140and executes a query for digital media (e.g., videos) associated withthe one or more keyword searches. In some embodiments, videosegmentation program 122 communicates with server application 142 toperform a query of database 144 for videos associated with the one ormore keyword searches. In various embodiments, video segmentationprogram 122 communicates a set of program instructions to serverapplication 142 instructing server application 142 to execute a queryfor videos associated with the one or more keyword searches.Additionally, the set of program instructions instruct serverapplication 142 to communicate the digital media results to videosegmentation program 122. In some embodiments, video segmentationprogram 122 access database 144 executing on SAN 140 and executes queryfor videos associated with the one or more keyword searches andcommunicates with server application 142 to retrieve the video resultsbased, at least, on the query of database 144.

In an alternative embodiment, a requestor of client device 130 performsa query for videos associated with the one or more keyword searches. Insome embodiments, client application 132 communicates a set of programinstructions to server application 142 executing on SAN 140 instructingserver application 142 to perform a query on database 144 for videosassociated with the one or more keyword searches. In variousembodiments, server application 142 locates various videos associatedwith the keyword searches (e.g., list of videos, digital media results)and communicates the digital media results to client applicationexecuting on client device 130. Client application 132 generates a userrequest based, at least, on the results of the video query (e.g., listof videos, digital media results) and communicates the user request tocomputer system 120.

In various embodiments, video segmentation program 122 identifies thedigital media results which includes, but it not limited to, (i) one ormore videos, (ii) one or more video direct accesses, and/or (iii) one ormore crowd-based comments. Additionally, video segmentation program 122analyzes the digital media results based, at least, on the content ofthe user request. The present invention recognizes that the inventionutilizes, but is not limited to, one or a combination of: speech to textprocessing, video image processing, natural language processing,Word2vec, cognitive AI processing, etc., to analyze the various digitalmedia results.

In various embodiments of the present invention, video segmentationprogram 122 analyzes the crowd-based comments and video direct accesses,wherein video segmentation program 122 is searching for a thresholdvalue that aligns with the keywords associated with the user request.One having ordinary skill in the art would understand that videosegmentation program 122 utilizes algorithms including, but is notlimited to, image processing, video processing, machine vision, naturallanguage processing, cognitive AI, etc., to analyze the digital mediadata (e.g., crowd-based comments, video direct accesses, etc.). In someembodiments, video segmentation program 122 analyzes the digital mediadata to identify videos that meet a threshold value that align with theone or more keywords of the user request.

In various embodiments, based, at least in part, on the analyzationperformed by video segmentation program 122, video segmentation program122 identifies one or more videos and the one or more digital mediaassociated with the one or more videos that reaches a threshold value.In some embodiments, video segmentation program 122 identifies one ormore videos that contains one or more digital media data (e.g.,crowd-based comments, video direct accesses, etc.) that correlate withthe one or more keywords associated with the user request.

In one embodiment and example, video segmentation program 122 receives auser request from client device 130, requesting video segmentationprogram 122 to generate a video summary associated with a commercialproduct based, at least, on (i) one or more videos. (ii) crowd-basedcomments, (iii) and video direct accesses. In various embodiments, videosegmentation program 122 executes a query on database 144 for variousvideos and one or more digital media data. In some embodiments, videosegmentation program 122 communicates with server application 142 toperform the query on database 144 and, additionally, server application142 receives a set of program instructions instructing serverapplication 142 to communicate the digital media results associated withthe query to video segmentation program 122. Video segmentation program122 receives the digital media results and analyzes the digital mediadata. In various embodiments, video segmentation program 122 identifiesone or more crowd-based comments associated with the videos of thedigital media results that articulate a positive review of thecommercial product and/or a negative review of the commercial product(i.e., content related with user request). In some embodiments, the oneor more identified crowd-based comments include one or more video directaccess that direct the viewer of the video to a specific point-in-timeof the video. In some embodiments video segmentation 122 identifies thata video direct access is associated with a crowd-based comment (e.g.,positive feedback, negative feedback, or neutral feedback).

In various embodiments, video segmentation program 122 retrieves thevideo file associated with the identified digital media data fromdatabase 144, and video segmentation program 122 clips the segment ofthe video file based, at least, on the identification of content relatedwith the user request associated with (i) one or more crowd-basedcomments and (ii) one or more video direct accesses. Video segmentationprogram 122 stores the segmented video on database 126. Additionally,video segmentation program 122 retrieves one or more video filesassociated with the identified digital media data, and clips additionalsegments from the one or more video files based, at least, on theidentification of content related with the user request associated with(i) one or more crowd-based comments and (ii) one or more video directaccesses. Video segmentation program 122 stores the additional one ormore segmented videos on database 126 for subsequent use. The presentinvention recognizes that a segmented video represents a portion and/orsegment of video associated with the original video file, wherein videosegmentation program 122 clipped the segmented video from the originalvideo file.

In various embodiments of the present invention, video segmentationprogram 122 retrieves one or more video segments from database 126 thatare associated with the user request. Video segmentation program 122assigns a threshold value (e.g., a rank) to the one or more videosegments from a highest to lowest value (i.e., provide the mostinformation to least information) based, at least in part, on athreshold value identified on the analyzation of the digital media databy video segmentation program 122. In some embodiments, videosegmentation program 122 retrieves one or more video segments andanalyzes the video segments and the associated crowd-based comments todetermine the most similar and least similar video segments based, atleast, on the user request.

The present invention recognizes that video segmentation program 122utilizes one or a combination of: video processing, image processing,natural language processing, Word2vec, machine vision, etc., to analyzethe video segments and associated with crowd-based comments. In variousembodiments, video segmentation program 122 analyzes the one or morevideo segments and crowd-based comments and determines which videosegments and associated crowd-based comments provide the mostinformation relative to the user request, and which video segments andassociated crowd-based comments provide the least information relativeto the user request. In various embodiments, video segmentation program122 determines the threshold value of one or more video segments based,at least, on the following, but is not limited to, (i) the content ofthe video segment, (ii) the positive and/or negative feedback of the oneor more crowd-based comments, (iii) the quantity of the one or morecrowd-based comments.

In various embodiments, video segmentation program 122 generates aranked list of one or more video segments and associated crowd-basedcomments, respectively. The present invention recognizes that the rankedlist is generated based, at least in part, on the threshold valueassigned to the one or more video segments and associated crowd-basedcomments.

In various embodiments of the present invention, video segmentationprogram 122 generates a video summary that includes, but is not limitedto, (i) one or more video segments, (ii) one or more crowd-basedcomments associated with the one or more video segments, and (iii)various video summary data associated with the video segments (i.e.,title of video files, length of video segments, URL links to originalvideo files, etc.). In various embodiments, video segmentation program122 generates the video summary by splicing the one or more videosegments together to form one cohesive video. In various embodiments,video segmentation program 122 embeds video direct access into the videosummary associated, at least, with the one or more crowd comments toallow the user to skip to specific portions of the video summary.Additionally, video segmentation program 122 embeds hyperlinks of theURL links to the original video files within the video summary.

In various embodiments, video segmentation program 122 communicates thevideo summary to client application 132 with program instructionsinstructing client application 132 to populate the video summary oncomputer interface 134 for the requestor to view. In some embodiments,video segmentation program 122 communicates a set of programinstructions instructing client application 132 to record the useractivity with the video summary which includes, but is not limited to,the quantity of each video segment viewed by the requestor and whetherthe requestor accessed one or more embedded hyperlinks associated with avideo segment and crowd-based comment, respectively. Additionally, videosegmentation program 122 further communicates a set of programinstructions to client application 132 instructing client application132 to communicate one or more user activity to video segmentationprogram 122. In various embodiments, video segmentation program 122receives one or more user activity from client application 132 andstores the one or more user activity on database 126. In alternativeembodiment, video segmentation program 122 communicates the videosummary to video sharing application 124 with program instructionsinstructing video sharing application 124 to upload the video summary toa video sharing platform (e.g., SAN 140).

In various embodiments of the present invention, video segmentationprogram 122 receives user activity based, at least in part, on theuser's activity of the, at least, one video summary, as recognizedabove. Video segmentation program 122 analyzes the user's activity andidentifies data including, but not limited to, (i) the quantity of timeviewed of each video segment, respectively, and (ii) the amount ofembedded hyperlinks associated with the, at least, one video summary theuser accessed. Video segmentation program 122 determines based, atleast, on the analyzation of the user activity whether to adjust, alter,and/or update the content of the video summary. The present inventionrecognizes video segmentation program 122 analyzes the user activityassociated with the content of the video summary, and in variousembodiments, video segmentation program 122 identifies user activitythat includes, but is not limited to, (i) the user views and/or accessescontent contained within the video summary, and/or (ii) the user doesnot views and/or access content contained within the video summary. Invarious embodiments, video segmentation program 122 analyzes the useractivity and identifies that the user viewed the video summary in itsentirety and accessed a threshold value of embedded hyperlinksassociated with the video summary. Based, at least, on thisidentification, video segmentation program 122 determines that the videosummary does not need to be updated.

In various embodiments of the present invention, video segmentationprogram 122 analyzes the user activity and identifies data including,but not limited to, (i) the quantity of time viewed of each videosegment, respectively, and (ii) the amount of embedded hyperlinksassociated with the, at least, one video summary the user accessed. Insome embodiments, video segmentation program 122 identifies that theuser watched a threshold value of time for one or more video segmentsand accessed a threshold value of embedded hyperlinks, wherein videosegmentation program 122 determines that the video summary does notrequire an update. In some embodiments, however, video segmentationprogram 122 analyzes the user activity and identifies that the user didnot view one or more video segments for a threshold value of time and/orthe user did not access a threshold value number of embedded hyperlinksassociated with the video summary, based, at least, on theidentifications by video segmentation program 122, video segmentationprogram 122 determines that the video summary does require an update. Invarious embodiments, video segmentation program 122 analyzes the useractivity and identifies content contained within the video summary thatthe user views and/or accessed that reached a threshold value,additionally, video segmentation program 122 identifies contentcontained within the video summary that user did not view and/or accessto reach a threshold value, wherein video segmentation program 122determines that the content within the video summary that did not reacha threshold value of views and/or accesses must be updated.

In various embodiments, video segmentation program 122 updates the, atleast, one video summary, wherein video segmentation program 122determines that the, at least, one video summary requires an updatebased, at least in part, on the analyzation of the user activityassociated with a user viewing the video summary. Video segmentationprogram 122 communicates with server application 142 and executes, atleast, a second query on database 144, as recognized above, foradditional videos and crowd-based comments (e.g., additional digitalmedia data) based, at least, on (i) the user request and (ii) the useractivity associated with, at least, the one video summary. In someembodiments, video segmentation program 122 communicates a set ofprogram instructions to server application 142 to retrieve additionalvideos and crowd-based comments and communicate the additional videoscrowd-based comments to video segmentation program 122. In variousembodiments, video segmentation program 122 analyzes the additionaldigital media data and identifies (i) one or more crowd-based comments,(ii) one or more video direct accesses, and (iii) one or more videosegments that resembles content related with the user request.Additionally, video segmentation program 122 identifies additionalcontent related with the user request that is distinct from the contentwithin the video summary that did not reach a threshold value of viewsand/or accesses. In various embodiments, video segmentation program 122retrieves the identified additional content that is distinct from thecontent within the video summary that did not reach a threshold value,wherein, video segmentation program 122 clips the content within thevideo summary that did not reach the threshold value and replaces withthe identified additional content. In some embodiments, videosegmentation program 122 analyzes the identified additional content andthe content that did reach a threshold value and assigns a rank to thecontent, as recognized above. Video segmentation program 122 organizesthe content in a list from the highest to lowest rank, as recognizedabove. Video segmentation program 122 stores the updated video summaryon database 126. In some embodiments, video segmentation program 122communicates the updated video summary to client device 130, asdiscussed above. In additional embodiments, video segmentation program122 communicates the updated video summary to video sharing application124 to be uploaded to the video sharing website, as discussed above.

In one embodiment and example of the present invention, a user of clientdevice 130 (e.g., requestor) generates a user request to compare zipperbackpacks that includes, but is not limited to, keywords (e.g., schoolbackpack, rucksack, laptop case, sling bag, tote backpack, etc.),description of backpack (e.g., size, shape, color, etc.), and additionalfeatures (e.g., demonstration of use of the various backpacks). Videosegmentation program 122 receives the user request and communicates withserver application 142 to execute a query for videos that meet thecriteria defined within the user request. In various embodiments, videosegmentation program 122 receives digital media data that includes, butis not limited to: (i) one or more videos, (ii) one or more crowd-basedcomments, (iii) one or more video direct accesses, and (iv) various metaassociated with the one or more videos, one or more crowd-basedcomments, and the one or more video direct accesses. Upon receipt of thedigital media data, video segmentation program 122 analyzes the digitalmedia data.

In the embodiment and example presented, video segmentation program 122analyzes the digital media data to identify various feedback associatedwith the user request. In some embodiments, video segmentation program122 analyzes the one or more crowd-based comments and identifies one ormore crowd-based comments that provide substantive feedback (e.g.,positive and/or negative feedback) associated with a consumer product(e.g., a zipper backpack) within a video. In some embodiments, videosegmentation program 122 identifies that, at least, one video directaccess is associated with, at least, one crowd-based comment. Videosegmentation program 122 utilizes the video direct access to facilitatethe clipping of the video file. Video segmentation program 122 analyzesthe video file and identifies a segment of the video file to clip based,at least in part, on (i) a crowd-based comment and (ii) a video directaccess. In the embodiment presented, video segmentation program 122identified a video segment that depicted the illustration andrepresentation of a zipper backpack and the functionality of the zipper,respectively, that matched a threshold value of the criteria of the userrequest. Video segmentation program 122 generates, at least, on videosegment. The present invention further recognizes that videosegmentation program 122 analyzes one or more videos associated with (i)one or more crowd-based comments and (ii) one or more video directaccess to generate one or more video segments. Video segmentationprogram 122 operates to splice the one or more video segments togetherto create one cohesive video. Additionally, video segmentation program122 generates a video summary associated with, at least, (i) one or morevideo segments related to zipper backpacks, (ii) one or more crowd-basedcomments that include, but not limited to, substantive feedback, (iii)one or more video direct accesses associated, at least, with the videofiles of the one or more video segments, and (iv) various summary dataassociated with the video segments (i.e., title of video files, lengthof video segments, URL links to original video files, etc.). Videosegmentation program 122 communicates the video summary to client device130 with program instructions to populate computer interface 134 withthe video summary. In some embodiments, video segmentation program 122communicates the video summary to video sharing application 124 to becommunicated to SAN 140 and uploaded to the video sharing website.

In one embodiment and example, a user of client device 130 (e.g.,requestor) generates a user request to compare volume and cargo space ofthe interior of a vehicle that includes, but is not limited to,keywords. Video segmentation program 122 receives the user request andcommunicates with server application 142 to execute a query for videosthat meet the criteria defined within the user request. In variousembodiments, video segmentation program 122 receives digital media datathat includes, but is not limited to: (i) one or more videos, (ii) oneor more crowd-based comments, (iii) one or more video direct accesses,and (iv) various meta associated with the one or more videos, one ormore crowd-based comments, and the one or more video direct accesses.Upon receipt of the digital media data, video segmentation program 122analyzes the digital media data.

In the embodiment and example presented, video segmentation program 122analyzes the digital media data to identify various feedback associatedwith the user request. In some embodiments, video segmentation program122 analyzes the one or more crowd-based comments and identifies one ormore crowd-based comments that provide substantive feedback (e.g.,positive and/or negative feedback) associated with a one or morevehicles within a video. In some embodiments, video segmentation program122 identifies that, at least, one video direct access is associatedwith, at least, one crowd-based comment. Video segmentation program 122utilizes the video direct access to facilitate the clipping of the videofile. Video segmentation program 122 analyzes the video file andidentifies a segment of the video file to clip based, at least in part,on (i) a crowd-based comment and (ii) a video direct access. Videosegmentation program 122 generates a video summary associated with, atleast, (i) one or more video segments related to vehicles, (ii) one ormore crowd-based comments that include, but not limited to, substantivefeedback, (iii) one or more video direct accesses associated, at least,with the video files of the one or more video segments, and (iv) varioussummary data associated with the video segments (i.e., title of videofiles, length of video segments, URL links to original video files,etc.). Video segmentation program 122 communicates the video summary toclient device 130 with program instructions to populate computerinterface 134 with the video summary.

In various embodiments of the present invention, video segmentationprogram 122 receives one or more digital media data. In variousembodiments, video segmentation program 122 analyzes the crowd-basedinteractions associated with the one or more videos. The presentinvention recognizes that the crowd-based interactions include one of,or a combination of: crowd-based interactions with various segments ofthe one or more videos, one or more crowd-based comments, one or morecrowd-based reactions (e.g., like, dislike, emotes, shares, etc.). Insome embodiments, video segmentation program 122 determines one or morevideo segmentations based, at least in part, on (i) the one or morecrowd-based interactions associated with the one or more video segments.

FIG. 2 is a flowchart depicting operations for executing a video summarysystem for computing environment 100, in accordance with an illustrativeembodiment of the present invention. More specifically, FIG. 2 , depictscombined overall operations, 200, of video segmentation program 122executing on computer system 120 to manage generation of video summariesbased, at least, on digital media data. In some embodiments, operations200 represents logical operations of video segmentation program 122,wherein interactions between client application 132 executing on clientdevice 130 and server application 142 executing on SAN 140 representinteractions between logical units executing on computer system 120. Itshould be appreciated that FIG. 2 provides an illustration of oneimplementation and does not imply any limitations with regard to theenvironment in which different embodiments may be implemented. Manymodifications to the depicted environment may be made. In one embodimentof flowchart 200, the series of operations can be performed in anyorder. In another embodiment of flowchart 200, the series of operationscan be performed in any order. In another embodiment, the series ofoperations, in flowchart 200, can be terminated at any operation.Additionally, any operations of flowchart 200, can be resumed at anytime.

In operation 202, video segmentation program 122 receives a user requestfrom client device 130 to generate a video summary based, at least, on(i) one or more keywords and/or (ii) a query search associated withdigital media data. In various embodiments of the present invention,video segmentation program 122 communicates with server application 142to execute a query on database 144 based, at least, on the one or morekeywords. The present invention recognizes that a query on databaseincludes, but is not limited to, searching for data stored on thedatabase in view of one or more keywords. In some embodiments, based, atleast, on a set of program instructions, server application 142retrieves one or more digital media data associated with the query andcommunicates the one or more digital media data to video segmentationprogram 122. In some embodiments video segmentation program 122 receivesthe digital media data.

In various embodiments of the present invention, video segmentationprogram 122 analyzes the digital media results and identifies data whichincludes, but is not limited to, (i) one or more videos, (ii) one ormore video direct accesses, and/or (iii) one or more crowd-basedcomments. Additionally, video segmentation program 122 analyzes thedigital media results based, at least, on the content (e.g., keywords)of the user request. The present invention recognizes that the inventionutilizes one or a combination of but is not limited to: speech to textprocessing, video image processing, natural language processing,cognitive AI processing, etc. to analyze the various digital mediaresults.

In various embodiments of the present invention, video segmentationprogram 122 analyzes the (i) one or more videos, (ii) one or more videodirect accesses, and/or (iii) one or more crowd-based comments. Videosegmentation program 122 analyzes the above data to identify contentthat reaches a threshold value of similarity to the one or more keywordsfrom the user request to generate a video summary. In variousembodiments, video segmentation program 122 analyzes the data toidentify one or more videos, one or more video direct access, and/or oneor more crowd-based comments that depict content based, at least, on thevideo summary the user request asks to generate. The present inventionrecognizes that video segmentation program 122 utilizes algorithmsand/or programs which include, but are not limited to: image processing,video processing, machine vision, natural language processing, cognitiveAI, etc., to analyze the digital media data (e.g., crowd-based comments,video direct accesses, etc.).

In one embodiment and example, the user request requests videosegmentation program 122 to generate a video summary of a commercialproduct. In some embodiments, video segmentation program 122 receivesdigital media data that includes, but is not limited to: (i) one or morevideos that include, but are not limited to, infomercials, consumerproduct reviews, etc., (ii) one or more crowd-based comments associatedwith the one or more videos, and (iii) one or more video direct accessesthat are associated with the one or more crowd-based comments. Videosegmentation program 122 analyzes the (i) one or more videos, (ii) oneor more crowd-based comments, and (iii) one or more video directaccesses, and identifies content within the digital media data thatdepicts and/or articulates information that relates to the commercialproduct the requestor is seeking additional information on.

The present invention recognizes that video segmentation program 122analyzes the digital media data to identify feedback (e.g., positiveand/or negative feedback) that can assist the requestor by providinginformation regarding the commercial product. In various embodiments,video segmentation program 122 identifies feedback from one or morecrowd-based comments that include one or more video direct accesses thatare associated with, at least, one video. Video segmentation program 122determines that one or more video segments should be clipped from the,at least, one video file based, at least in part, on identification of(i) the feedback and (ii) the one or more video direct accessesassociated with the feedback with the commercial product. In someembodiments, video segmentation program 122 identifies feedbackcontained within, at least, one video (e.g., infomercial, consumerproduct review, etc.) and determines that one or more video segmentsshould be clipped from the video file. The present invention recognizesthat video segmentation program 122 utilizes programs and/or algorithmsthat include, but are not limited to: machine vison, video processing,image processing, natural language processing, speech to text processingto determine whether content contained within a video should be clippedinto one or more video segments.

In operation 204, video segmentation program 122 identifies one or morevideo segments associated with the digital media data that reaches athreshold value to generate a video summary. In various embodiments,video segmentation program 122 analyzes the one or more crowd-basedcomments and video direct accesses that are associated with the one ormore crowd-based comments and identifies one or more video segments thatreaches a threshold value. Video segmentation program 122 retrieves thevideo file associated with the (i) one or more videos, (ii) the one ormore crowd-based comments, and (iii) one or more video direct accessesfrom database 144. Upon retrieving the video file, video segmentationprogram 122 clips the segment of the video file associated with thevideo segment identified by analyzing (i) the video, (ii) the one ormore crowd-based comments, and (iii) the one or more video directaccesses. Video segmentation program 122 stores the one or more videosegments on database 126. The present invention recognizes that theremay be one or more video segments clipped from, at least, one video thatvideo segmentation program 122. Additionally, the present invention alsorecognizes that there may be more than one video file that videosegmentation program 122 clips.

In operation 206, video segmentation program 122 retrieves the one ormore video segments from database 126 and analyzes the one or more videosegments to generate a ranked list from a highest to lowest value (i.e.,provide the most information to least information). In variousembodiments of the present invention, video segmentation program 122retrieves the various digital media data associated with the one or morevideo segments and analyzes the content of the digital media data toidentify data points that are most similar to user request. The presentinvention recognizes that video segmentation program 122 utilizes one ora combination of: video processing, image processing, natural languageprocessing, machine vision, etc. to analyze the video segments andassociated with crowd-based comments. In various embodiments, videosegmentation program 122 analyzes the one or more video segments andcrowd-based comments and determines which video segments and associatedcrowd-based comments provide the most information relative to the userrequest, and which video segments and associated crowd-based commentsprovide the least information relative to the user request.

In various embodiments, video segmentation program 122 identifies one ormore video segments based, at least, on the following, but is notlimited to, (i) the content of the video segment, (ii) the feedback(i.e., positive and/or negative feedback) of the one or more crowd-basedcomments, (iii) the quantity of the one or more crowd-based comments. Invarious embodiments, video segmentation program 122 utilizes afeedforward neural network to determine a weighting factor associatedwith the feedback associated with a set of video segments. In someembodiments, video segmentation program 122 arranges the digital mediadata (e.g., (i) content of the video segment, (ii) the feedback of theone or more crowd-based comments, and (iii) the quantity of the one ormore crowd-based comments into neurons in a layered feedforward topologynetwork. In some embodiments, video segmentation program 122 monitorsthe neurons in the neural network, as each input connects to neuronsbetween, at least, a first layer, one or more hidden layers, and asecond layer. One having ordinary skill in the art would understand thatthe multilayer perception neural network arranges input data into aplurality of neurons in the first layer of the neutral network andarranges the output data of the first layer into the input of the secondlayer to create a fully connected neural network. Video segmentationprogram 122 receives output data with weights and thresholds describingthe free parameters of the I/O data (e.g., I/O data included in thedigital media data). One having ordinary skill in the art wouldunderstand that the weights and thresholds describing the freeparameters of the I/O data represent the change in the variables fromthe input data to the output data of the neural network.

In various embodiments, video segmentation program 122 includes aconvolutional neural network (CNN). Wherein the CNN consist of I/O data,as well as multiple hidden layers of neurons (i.e., RELU layer). Videosegmentation program 122 analyzes the (i) the input data of themultiplayer perceptron neural network (MLP) and (ii) the change in theoutput variables, at least in part. In various embodiments, videosegmentation program 122 analyzes the output data of the CNN, whereinthe output data represents a scaled numeric variable based, at least, on(i) the input data and (ii) the weights assigned to each input datathrough the one or more layers of the CNN. The present inventionrecognizes that the output data is associated with one or more videosegments. Video segmentation program 122 organizes the output data(i.e., one or more video segments) in a ranked list based, at least, onthe scaled numeric variable. In various embodiments, the scaled numericvariable represents a ranked value that determines how relevant and/orsimilar the video segment is compared to various other video segments,in which the user request requests a video summary compiled of digitalmedia data.

Video segmentation program 122 generates a video summary that includes,but is not limited to, (i) one or more ranked video segments, (ii) oneor more crowd-based comments associated with the one or more videosegments, and (iii) various summary data associated with the videosegments (i.e., title of video files, length of video segments, URLlinks to original video files, etc.). In various embodiments, videosegmentation program 122 generates the video summary by splicing the oneor more video segments together to form on cohesive video. In variousembodiments, video segmentation program 122 embeds one or more videodirect accesses into the video summary associated, at least, with theone or more crowd-based comments to allow the user to skip to specificsegments of the video summary. Additionally, video segmentation program122 embeds hyperlinks of the URL links to the original video fileswithin the video summary.

FIG. 3 depicts a flowchart depicting operations for executing a videosummary system for computing environment 100, in accordance with anillustrative embodiment of the present invention. More specifically,FIG. 3 , depicts combined overall operations, 300, of video segmentationprogram 122 (executing on computer system 120). In some embodiments,operations 300 represents logical operations of video segmentationprogram 122, wherein interactions between video segmentation program 122of computer system 120, client application 132 of client device 130 andserver application 142 of SAN 140 represents logical units executing oncomputer system 120. Further, operations 300 can include a portion orall of combined overall operations of 200. It should be appreciated thatFIG. 3 provides an illustration of one implementation and does not implyany limitations with regard to environments in which differentembodiments may be implemented. Many modifications to the depictedenvironment may be made. In one embodiment of flowchart 300, the seriesof operations, of flowchart 300, can be performed simultaneously.Additionally, the series of operations, in flowchart 300, can beterminated at any operation. In addition to the features previouslymentioned, any operation, of flowchart 300, can be resumed at any time.

In operation 302, video segmentation program 122 analyzes digital mediadata. In various embodiments of the present invention, videosegmentation program 122 identifies one or more videos which includes,but is not limited to, (i) one or more crowd-based comments and (ii) oneor more direct video accesses. Video segmentation program 122 analyzesthe videos and the one or more crowd-based comments. In someembodiments, video segmentation program 122 identifies one or morecrowd-based comments that include content that provides substantivefeedback (e.g., positive and/or negative feedback) regarding the video.Additionally, video segmentation program 122 analyzes the one or morecrowd-based comments to determine if a video direct access is includedwith the crowd-based comments. In some embodiments, video segmentationprogram 122 determines that a video direct access exists, then videosegmentation program 122 utilizes that video direct access to clip thevideo file, at least, starting from the point in which time isassociated with the video direct access, as discussed above. One havingordinary skill in the art would understand that a video direct accessdirects a viewer of a video to a specific point in time during theduration of the video. In another embodiment, video segmentation program122 analyzes the video utilizing one or a combination of: imageprocessing, video processing, machine vision, cognitive AI processing,natural language processing, speech to text processing, etc., todetermine which, if any, segments of the video provide substantivefeedback regarding the content of the video. In various embodiments,video segmentation program 122 identifies that one or more segments of avideo exist that provide substantive feedback regarding the content ofthe video.

In operation 304, video segmentation program 122 generates one or morevideo segments associated with the digital media data. In variousembodiments, video segmentation program 122 generates one or more videosegments based, at least in part, on the analyzation of the videoitself. In these embodiments, the present invention recognizes thatvideo segmentation program 122 analyzes the video in its entirety, anddetermines, at least, that one or more segments of the video reach athreshold value to provide information to the requestor. In theseembodiments, video segmentation program 122 clips the video file, for atleast, a determined period of time associated with the determinationstep. Video segmentation program 122 stores the one or more videosegments on database 126. The present invention recognizes that therecould be any number of videos that video segmentation program 122analyzes and the present invention does not limit itself to a specificnumber.

In various embodiments, video segmentation program 122 analyzes one ormore crowd-based comments that may include one or more video directaccesses. Video segmentation program 122 analyzes the one or morecrowd-based comments, as discussed above, and determines whether the oneor more crowd-based comments include substantive feedback (e/g/.positive and/or negative feedback) that would provide information to therequestor based, at least, on the user request. In some embodiments, theone or more crowd-based comments include one or more video directaccesses. Video segmentation program 122 analyzes the time stampassociated with the one or more video direct accesses and utilizes theone or more video direct accesses to analyze the portion of video thatthe one or more video direct accesses direct the viewer towards on thevideo. In various embodiments, video segmentation program 122 determinesthat (i) one or more crowd-based comments and/or (ii) the one or morevideo direct accesses contain substantive feedback and information thatwould be beneficial to the requestor based, at least, on the userrequest. Video segmentation program 122 clips the segment of the videothat (i) the one or more crowd-based comments and/or (ii) the one ormore video direct accesses direct the viewer towards on the video. Thepresent invention recognizes that video segmentation program 122 clipsone or more segments of one or more videos based, at least, on thecontent of the one or more crowd-based comments, and the varioussegments of the video that the one or more video direct accesses. Videosegmentation program 122 stores the one or more video segments ondatabase 126. In various embodiments, as recognized above, videosegmentation program 122 assigns a threshold value (e.g., a rank) to theone or more video segments and arranges the one or more video segmentsin a descending order from a highest to lowest rank value.

In operation 306, video segmentation program 122 generates (i) the oneor more video segments, (ii) the one or more crowd-based commentsassociated with the one or more video segments, and (iii) various videosummary data associated with the video segments (i.e., title of videofiles, length of video segments, URL links to original video files,etc.). In various embodiments, video segmentation program 122 generatesthe video summary by splicing the one or more video segments together toform, at least, one cohesive video. In various embodiments, videosegmentation program 122 embeds video direct access into the videosummary associated, at least, with the one or more crowd comments toallow the user to skip to specific portions of the video summary.Additionally, video segmentation program 122 embeds hyperlinks of theURL links to the original video files within the video summary.

FIG. 4 depicts a flowchart depicting operations for executing an updatedvideo summary for computing environment 100, in accordance with anillustrative embodiment of the present invention. More specifically,FIG. 4 depicts combined overall operations, 400, of video segmentationprogram 122 (executing on computer system 120). In some embodiments,operations 400 represents logical operations of video segmentationprogram 122, wherein interactions between video segmentation program 122of computer system 120, client application 132 of client device 130, andserver application of SAN 140 represents logical units executing oncomputer system 120. Further, operations 400 can include a portion orall of combined overall operations of 200 and operations of 300. Itshould be appreciated that FIG. 4 provides an illustration of oneimplementation and does not imply any limitations with regard to theenvironments in which different embodiments may be implemented. Manymodifications to the depicted environment may be made. In one embodimentof flowchart 400, the series of operations can be performed in anyorder. In another embodiment, the series of operations, of flowchart400, can be performed simultaneously. Additionally, the series ofoperations, in flowchart 400, can be terminated at any operation. Inaddition to the features previously mentioned, any operation, offlowchart 400, can be resumed at any time.

In operation 402, video segmentation program 122 registers user activityfrom, at least, client application 132. Video segmentation program 122identifies which, if any, of the one or more video direct accessesassociated with the, at least, one video summary has been accessed.Video segmentation program 122 records the quantity of times the one ormore video direct accesses had been utilized by the viewer of the videosummary.

In operation 404, video segmentation program 122 analyzes the videosummary activity (e.g., user activity). In various embodiments, videosegmentation program 122 identifies data including, but is not limitedto, (i) the quantity of time viewed of each video segment, respectively,and (ii) the amount of embedded hyperlinks associated with the, atleast, one video summary the user accessed. Additionally, videosegmentation program 122 identifies the extent to which the viewer ofthe video summary engaged with the video summary. In variousembodiments, video segmentation program 122 determines that if a viewerviewed the video summary for a threshold value of time, then the viewerfound the information beneficia. Additionally, if the viewer accessed athreshold value of URL hyperlinks associated with the video summary,then the viewer found the information beneficial. In some embodiments,video segmentation program 122 determines that a viewer of the videosummary did not find the information beneficial, if the viewer did notview the video summary for a threshold value of time and/or if theviewer did not access a threshold quantity of URL hyperlinks. Thepresent invention recognizes that if a viewer views the video summary infull or for a threshold period of time and/or accesses a thresholdquantity of URL hyperlinks it is representative that the viewer foundthe content and information contained within the video summarybeneficial. In some embodiments, however, video segmentation program 122determines that a viewer does not find the content and informationbeneficial if the viewer does not view the video summary for a thresholdperiod of time and/or does not access a threshold quantity of URLhyperlinks. If video segmentation program 122 determines that the viewerdid not find the content and information beneficial based, at least, onthe threshold levels discussed above, then video segmentation program122 updates the video summary.

In operation 406, video segmentation program 122 updates the videosummary based, at least, on video segmentation program 122 determiningthat the viewer did not find the content and information containedwithin the video summary beneficial, as discussed above. In variousembodiments, video segmentation program 122 determines that the, atleast, one video summary needs to be updated, at least, in full orin-part. In various embodiments, video segmentation program 122communicates with server application 142 and executes, at least, asecond query on database 144 based, at least, on the user request forthe one video summary. In various embodiments, the second query isexecuted in search of additional videos, crowd-based comments, and oneor more video direct accesses (e.g., additional digital media data),wherein the second query is based, at least, on (i) the user request and(ii) the user activity associated with, at least, the one video summary.In some embodiments, video segmentation program 122 communicates a setof program instructions to server application 142 to retrieve additionaldigital media data and communicate the additional digital media to videosegmentation program 122.

In various embodiments, video segmentation program 122 analyzes theadditional digital media data and identifies (i) one or more crowd-basedcomments, (ii) one or more video direct accesses, and (iii) one or morevideo segments that is associated, at least in part, with the userrequest. Additionally, video segmentation program 122 identifiesadditional content related with the user request that is distinct fromthe content within the video summary that video segmentation program 122identified that did not reach a threshold value, as discussed above.Video segmentation program 122 updates the video summary by removing thedigital media data that did not reach a threshold value and replacesthat content with the additional digital media data identified by videosegmentation program 122. In various embodiments, video segmentationprogram 122 communicates the updated video summary to client device 130for the requestor of client deice 130 to view. In some embodiments,video summary segmentation program 122 communicates the updated videosummary to database 144 to be stored and accessed on the video-sharingwebsite.

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 5 , illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 comprises one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 5 are intended to be illustrative only and that computing nodes10 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 6 , a set of functional abstraction layersprovided by cloud computing environment 50 (FIG. 5 ) is shown. It shouldbe understood in advance that the components, layers, and functionsshown in FIG. 6 are intended to be illustrative only and embodiments ofthe invention are not limited thereto. As depicted, the following layersand corresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and providing soothing output 96.

FIG. 7 depicts a block diagram, 700, of components of computer system120, client device 130, and SAN 140, in accordance with an illustrativeembodiment of the present invention. It should be appreciated that FIG.7 provides only an illustration of one implementation and does not implyany limitations with regard to the environments in which differentembodiments may be implemented. Many modifications to the depictedenvironment may be made.

Computer system 120, client device 130, and SAN 140 includescommunications fabric 702, which provides communications betweencomputer processor(s) 704, memory 706, persistent storage 708,communications unit 710, and input/output (I/O) interface(s) 712.Communications fabric 702 can be implemented with any architecturedesigned for passing data and/or control information between processors(such as microprocessors, communications and network processors, etc.),system memory, peripheral devices, and any other hardware componentswithin a system. For example, communications fabric 702 can beimplemented with one or more buses.

Memory 706 and persistent storage 708 are computer-readable storagemedia. In this embodiment, memory 706 includes random access memory(RAM) 714 and cache memory 716. In general, memory 706 can include anysuitable volatile or non-volatile computer-readable storage media.

Video segmentation program 122, video sharing application 124, database126, client application 132, computer interface 134, server application142, and database 144, are stored in persistent storage 708 forexecution and/or access by one or more of the respective computerprocessors 704 via one or more memories of memory 706. In thisembodiment, persistent storage 708 includes a magnetic hard disk drive.Alternatively, or in addition to a magnetic hard disk drive, persistentstorage 708 can include a solid state hard drive, a semiconductorstorage device, read-only memory (ROM), erasable programmable read-onlymemory (EPROM), flash memory, or any other computer-readable storagemedia that is capable of storing program instructions or digitalinformation.

The media used by persistent storage 708 may also be removable. Forexample, a removable hard drive may be used for persistent storage 708.Other examples include optical and magnetic disks, thumb drives, andsmart cards that are inserted into a drive for transfer onto anothercomputer-readable storage medium that is also part of persistent storage708.

Communications unit 710, in these examples, provides for communicationswith other data processing systems or devices, including resources ofnetwork 110. In these examples, communications unit 710 includes one ormore network interface cards. Communications unit 710 may providecommunications through the use of either or both physical and wirelesscommunications links. Video segmentation program 122, video sharingapplication 124, database 126, client application 132, computerinterface 134, server application 142, and database 144 may bedownloaded to persistent storage X08 through communications unit X10.

I/O interface(s) 712 allows for input and output of data with otherdevices that may be connected to computer system 120, client device 130,and SAN 140. For example, I/O interface 712 may provide a connection toexternal devices 718 such as a keyboard, keypad, a touch screen, and/orsome other suitable input device. External devices 718 can also includeportable computer-readable storage media such as, for example, thumbdrives, portable optical or magnetic disks, and memory cards. Softwareand data used to practice embodiments of the present invention, e.g.,video segmentation program 122, video sharing application 124, database126, client application 132, computer interface 134, server application142, and database 144, can be stored on such portable computer-readablestorage media and can be loaded onto persistent storage 708 via I/Ointerface(s) 712. I/O interface(s) 712 also connect to a display 720.

Display 720 provides a mechanism to display data to a user and may be,for example, a computer monitor, or a television screen.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The programs described herein are identified based upon the applicationfor which they are implemented in a specific embodiment of theinvention. However, it should be appreciated that any particular programnomenclature herein is used merely for convenience, and thus theinvention should not be limited to use solely in any specificapplication identified and/or implied by such nomenclature.

It is to be noted that the term(s) such as, for example, “Smalltalk” andthe like may be subject to trademark rights in various jurisdictionsthroughout the world and are used here only in reference to the productsor services properly denominated by the marks to the extent that suchtrademark rights may exist.

What is claimed is:
 1. A computer-implemented method, the methodcomprising: analyzing, by one or more processors, one or more digitalmedia data to produce an analysis, wherein the one or more digital mediadata includes a video and a crowd-based interaction associated with thevideo, wherein the crowd-based interaction includes a comment;generating, by one or more processors, one or more video segments forthe video based on the one or more digital media data; and generating,by one or more processors, a video summary associated with the videobased on a user request.
 2. The computer-implemented method of claim 1,the method further comprising: receiving, by the one or more processors,a user request; executing, by the one or more processors, a query on adatabase based on the user request; identifying, by the one or moreprocessors, one or more digital media data associated with the userrequest; and receiving, by the one or more processors, the one or moredigital media data.
 3. The computer-implemented method of claim 1, themethod further comprising: analyzing, by the one or more processors, theone or more digital media data; identifying, by the one or moreprocessors, data associated with the one or more digital media data thatincludes (i) one or more videos, (ii) one or more crowd-based comments,and (iii) one or more video direct accesses; generating, by the one ormore processors, an analysis result by analyzing the one or more digitalmedia data using one or a combination of (i) natural languageprocessing, (ii) word2vec, (iii) cognitive AI processing, (iv) videoprocessing, and (v) machine vision; and identifying, by the one or moreprocessors, one or more user feedback associated with the one or moredigital media data based on the analysis result.
 4. Thecomputer-implemented method of claim 1, the method further comprising:analyzing, by the one or more processors, one or more user feedback;identifying, by the one or more processors, that the one or more userfeedback reaches a threshold value of similarity to the user request;and determining, by the one or more processors, that the one or moreuser feedback reaches a threshold value of similarity based on a resultof a semantic analysis of (i) the user request and (ii) the one or moreuser feedback.
 5. The computer-implemented method of claim 3, the methodfurther comprising: generating, by the one or more processors, one ormore video segments based on the user feedback associated with the oneor more digital media data; and generating, by the one or moreprocessors, an aggregate of the one or more video segments, wherein theaggregate includes a video summary direct access that is associated witha specific timestamp at which a given video segment begins.
 6. Thecomputer-implemented method of claim 5, the method further comprising:generating, by the one or more processors, the video summary whichincludes (i) the aggregate of the one or more video segments, and (ii)the one or more digital media data; generating, by the one or moreprocessors, one or more labels for the video summary based on (i) ananalysis of a crowd-based comment that points to a video segment and(ii) on one or a combination of wherein the one or more labels aregenerated based on one or a combination of words, hash representation,or n-gram structures; and generating, by the one or more processors, aranking for the one or more video segments based on a calculated scoreof the content of the one or more crowd-based comments, wherein thegiven calculated score is based on a feedforward neural network used todetermine a weighting factor associated with features associated withthe one or more video segments.
 7. The computer-implemented method ofclaim 5, the method further comprising: playing, by the one or moreprocessors, the video summary for one or more users; receiving, by theone or more processors, a user activity associated with the one or moreusers; analyzing, by the one or more processors, the user activity; andupdating, by the one or more processors, the one or more video segmentsassociated with the video summary based on the view time of the one ormore video segments.
 8. The computer-implemented method of claim 1, themethod further comprising: identifying, by the one or more processors,one or more crowd-based interactions associated with the one or morevideos; generating an analysis result by analyzing, by the one or moreprocessors, the one or more crowd-based interactions that include one ora combination of (i) crowd-based interaction with the one or morevideos, (ii) one or more crowd-based comments associated with one ormore segments of the one or more videos, or (iii) one or morecrowd-based reactions associated with the one or more videos; anddetermining, by the one or more processors, the one or more videosegments based on the analysis result.
 9. A computer program, thecomputer program product comprising: one or more computer-readablestorage media and program instructions stored on the one or morecomputer-readable storage media, the program instructions comprising:program instructions to analyze one or more digital media data toproduce an analysis, wherein the one or more digital media data includesa video and a crowd-based interaction associated with the video, whereinthe crowd-based interaction includes a comment; program instructions togenerate one or more video segments for the video based on the one ormore digital media data; and program instructions to generate a videosummary associated with the video based on a user request.
 10. Thecomputer program product of claim 9, the program instructions furthercomprising: program instructions to receive a user request; programinstructions to execute a query on a database based on the user request;program instructions to identify one or more digital media dataassociated with the user request; and program instructions to receivethe one or more digital media data.
 11. The computer program product ofclaim 9, the program instructions further comprising: programinstructions to analyze the one or more digital media data; programinstructions to identify data associated with the one or more digitalmedia data that includes (i) one or more videos, (ii) one or morecrowd-based comments, and (iii) one or more video direct accesses;program instructions to generate an analysis result by analyzing the oneor more digital media data using one or a combination of (i) naturallanguage processing, (ii) word2vec, (iii) cognitive AI processing, (iv)video processing, and (v) machine vision; and program instructions toidentify one or more user feedback associated with the one or moredigital media data based on the analysis result.
 12. The computerprogram product of claim 9, the program instructions further comprising:program instructions analyze one or more user feedback; programinstructions to identify that the one or more user feedback breaches athreshold value of similarity to the user request; and programinstructions to determine that the one or more user feedback reaches athreshold value of similarity based on a result of a semantic analysisof (i) the user request and (ii) the one or more user feedback.
 13. Thecomputer program product of claim 11, the program instructions furthercomprising: program instructions to generate one or more video segmentsbased on the user feedback associated with the one or more digital mediadata; and program instructions to generate an aggregate of the one ormore video segments, wherein the aggregate includes a video summarydirect access that is associated with a specific timestamp at which agiven video segment begins.
 14. The computer program product of claim 9,the program instructions further comprising: program instructions togenerate the video summary which includes (i) the aggregate of the oneor more video segments, and (ii) the one or more digital media data;program instructions to generate one or more labels for the videosummary based on (i) an analysis of a crowd-based comment that points toa video segment and (ii) on one or a combination of wherein the one ormore labels are generated based on one or a combination of words, hashrepresentation, or n-gram structures; and program instructions togenerate a ranking for the one or more video segments based on acalculated score of the content of the one or more crowd-based comments,wherein the given calculated score is based on a feedforward neuralnetwork used to determine a weighting factor associated with featuresassociated with the one or more video segments.
 15. The computer programproduct of claim 11, the program instructions further comprising:program instructions to play the video summary for one or more users;program instructions to receive a user activity associated with the oneor more users; program instructions to analyze the user activity; andprogram instructions to update the one or more video segments associatedwith the video summary based on the view time of the one or more videosegments.
 16. The computer program product of claim 9, the programinstructions further comprising: program instructions to identify one ormore crowd-based interactions associated with the one or more videos;program instructions to generate by the one or more processors, the oneor more crowd-based interactions that include one or a combination of(i) crowd-based interaction with the one or more videos, (ii) one ormore crowd-based comments associated with one or more segments of theone or more videos, or (iii) one or more crowd-based reactionsassociated with the one or more videos; and program instructions todetermine the one or more video segments based on the analysis result.17. A computer system, the computer system comprising: one or morecomputer processors; one or more computer readable storage medium; andprogram instructions stored on the computer readable storage medium forexecution by at least one of the one or more processors, the programinstructions comprising: program instructions to analyze one or moredigital media data to produce an analysis, wherein the one or moredigital media data includes a video and a crowd-based interactionassociated with the video, wherein the crowd-based interaction includesa comment; program instructions to generate one or more video segmentsfor the video based on the one or more digital media data; and programinstructions to generate a video summary associated with the video basedon a user request.
 18. The computer system of claim 17, the programinstructions further comprising: program instructions to receive a userrequest; program instructions to execute a query on a database based onthe user request; program instructions to identify one or more digitalmedia data associated with the user request; and program instructions toreceive the one or more digital media data.
 19. The computer system ofclaim 17, the program instructions further comprising: programinstructions to analyze the one or more digital media data; programinstructions to identify data associated with the one or more digitalmedia data that includes (i) one or more videos, (ii) one or morecrowd-based comments, and (iii) one or more video direct accesses;program instructions to generate an analysis result by analyzing the oneor more digital media data using one or a combination of (i) naturallanguage processing, (ii) word2vec, (iii) cognitive AI processing, (iv)video processing, and (v) machine vision; and program instructions toidentify one or more user feedback associated with the one or moredigital media data based on the analysis result.
 20. The computer systemof claim 17, the program instructions further comprising: programinstructions analyze one or more user feedback; program instructions toidentify that the one or more user feedback reaches a threshold value ofsimilarity to the user request; and program instructions to determinethat the one or more user feedback reaches a threshold value ofsimilarity based on a result of a semantic analysis of (i) the userrequest and (ii) the one or more user feedback.